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apify-audience-analysis

Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.

58

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/apify-audience-analysis/SKILL.md

The canonical home for this skill is apify-audience-analysis in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, actionable workflow with concrete commands, a useful actor-selection table, and error-handling feedback loops, with only minor gaps around input construction and output verification.

DimensionReasoningScore

Conciseness

The body is efficient and information-dense — the actor table is a compact decision reference and there is no padding explaining concepts Claude already knows — with only minor trimmable bits like the 'Copy this checklist' line and the '(No need to check it upfront)' aside, so it sits above the 'mostly efficient' anchor but not at fully lean.

4 / 5

Actionability

It provides concrete, copy-paste-ready bash commands (node --env-file=.env ... run_actor.js --actor ... --input ... and the mcpc tools-call invocation) with real Actor IDs, but the JSON_INPUT placeholder's construction is left implicit (delegated to Step 2's schema fetch), a minor gap keeping it below fully executable.

4 / 5

Workflow Clarity

A clear 5-step sequence with a progress checklist is present, and Step 2's schema fetch plus the Error Handling section supply a validation checkpoint and feedback loops for this batch scraping operation, so the batch-without-validation cap does not apply; the gap is only the lack of an explicit output-verification step before summarizing.

4 / 5

Progressive Disclosure

Content is well-organized into Prerequisites, Workflow, and Error Handling sections with a clearly signaled one-level script reference (${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js); it is not a 5 because the sizable 19-row actor table is arguably inline content that could live in a separate reference file for a skill over 50 lines.

4 / 5

Total

16

/

20

Passed

Description

61%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description clearly communicates a focused niche (audience analysis across four named social platforms) but omits any 'Use when...' trigger guidance, which caps completeness, and leans on the generic verb 'Understand' rather than enumerating concrete actions.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user wants to analyze followers, subscribers, or audience demographics and engagement on Facebook, Instagram, YouTube, or TikTok.'

Replace or supplement the generic verb 'Understand' with concrete actions such as 'Extract follower demographics, analyze engagement patterns, and report audience behavior'.

Include natural synonyms users actually say — 'followers', 'subscribers', 'engagement metrics', 'audience analytics' — to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

The description names the domain and several concrete facets ('demographics, preferences, behavior patterns, and engagement quality' across four named platforms), but relies on a single generic verb ('Understand') rather than multiple distinct concrete actions like extract/fill/merge, so it falls at the 'names domain and 1-2 concrete actions' anchor and not above.

3 / 5

Completeness

It clearly states what the skill does (audience analysis across platforms) but provides no 'Use when...' clause or equivalent trigger guidance, so per the rubric completeness is capped at 3 and cannot reach the 'both what and when' anchors above.

3 / 5

Trigger Term Quality

It includes natural terms a user would say ('audience demographics', 'engagement quality') plus four platform names (Facebook, Instagram, YouTube, TikTok), giving good keyword coverage; it stops short of 5 because common synonyms like 'followers', 'subscribers', or 'analytics' are missing.

4 / 5

Distinctiveness Conflict Risk

The combination of 'audience demographics/engagement' with four explicitly named platforms carves a clear niche with distinct triggers and minimal conflict risk; it is not a 5 because it could still overlap with broader social-media or Apify scraping skills.

4 / 5

Total

14

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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